/*
 * Copyright (c) 2022 Huawei Device Co., Ltd.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 *     http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 */
import { containsCollections, deepForEach, reduce } from '../../utils/collection.js';
import { arraySize } from '../../utils/array.js';
import { factory } from '../../utils/factory.js';
import { improveErrorMessage } from './utils/improveErrorMessage.js';
var name = 'mean';
var dependencies = ['typed', 'add', 'divide'];
export var createMean = /* #__PURE__ */factory(name, dependencies, _ref => {
  var {
    typed,
    add,
    divide
  } = _ref;

  /**
   * Compute the mean value of matrix or a list with values.
   * In case of a multi dimensional array, the mean of the flattened array
   * will be calculated. When `dim` is provided, the maximum over the selected
   * dimension will be calculated. Parameter `dim` is zero-based.
   *
   * Syntax:
   *
   *     math.mean(a, b, c, ...)
   *     math.mean(A)
   *     math.mean(A, dim)
   *
   * Examples:
   *
   *     math.mean(2, 1, 4, 3)                     // returns 2.5
   *     math.mean([1, 2.7, 3.2, 4])               // returns 2.725
   *
   *     math.mean([[2, 5], [6, 3], [1, 7]], 0)    // returns [3, 5]
   *     math.mean([[2, 5], [6, 3], [1, 7]], 1)    // returns [3.5, 4.5, 4]
   *
   * See also:
   *
   *     median, min, max, sum, prod, std, variance
   *
   * @param {... *} args  A single matrix or or multiple scalar values
   * @return {*} The mean of all values
   */
  return typed(name, {
    // mean([a, b, c, d, ...])
    'Array | Matrix': _mean,
    // mean([a, b, c, d, ...], dim)
    'Array | Matrix, number | BigNumber': _nmeanDim,
    // mean(a, b, c, d, ...)
    '...': function _(args) {
      if (containsCollections(args)) {
        throw new TypeError('Scalar values expected in function mean');
      }

      return _mean(args);
    }
  });
  /**
   * Calculate the mean value in an n-dimensional array, returning a
   * n-1 dimensional array
   * @param {Array} array
   * @param {number} dim
   * @return {number} mean
   * @private
   */

  function _nmeanDim(array, dim) {
    try {
      var sum = reduce(array, dim, add);
      var s = Array.isArray(array) ? arraySize(array) : array.size();
      return divide(sum, s[dim]);
    } catch (err) {
      throw improveErrorMessage(err, 'mean');
    }
  }
  /**
   * Recursively calculate the mean value in an n-dimensional array
   * @param {Array} array
   * @return {number} mean
   * @private
   */


  function _mean(array) {
    var sum;
    var num = 0;
    deepForEach(array, function (value) {
      try {
        sum = sum === undefined ? value : add(sum, value);
        num++;
      } catch (err) {
        throw improveErrorMessage(err, 'mean', value);
      }
    });

    if (num === 0) {
      throw new Error('Cannot calculate the mean of an empty array');
    }

    return divide(sum, num);
  }
});